One of the biggest disadvantages of hashtags is that they are site specific. It means that hashtags used in twitter – for example, do not relay to hashtags used in Facebook, Instagram and other sites: each website has its own hashtags system. When you click a hashtag on twitter for example, you get a page which consolidates all the twits that use this hashtags – in twitter. You won’t get Facebook posts which use that hashtags (obviously) or google search results which relay on that hashtags. This is how hashtags systems are designed and there is an obvious reason for that: each website would want to keep the users for itself. Facebook wouldn’t link to twitter pages (by design, not in twits) and twitter wouldn’t link (by design, not by posts) to Facebook pages. Otherwise, sites would “lose” traffic. Imagine that you clicked a hashtag in Twitter, and got links to Facebook posts… Twitter may go out of business if a post went viral in Facebook. And vice versa of course.
This specific way that hashtags are designed creates a big headache to social media networkers as they basically need to manage separate marketing streams for each social media channel. Some online tools like Buffer and Hotsuite resolve the posting troubles by consolidating scheduled posts into one interface, but the hashtags problem remained unsolved. In most cases, channel managers just do not manage hashtags: as it is too complicated to deal with so many separated channels.
Another insight is that you can’t really rely on market trends when you look at an apps which survey hashtags in a specific system. For example, if the Twitter trends pages tell you that “#VoteHillaryClinton” is the most trending hashtag at the moment, it is obviously taking only Twitter into account. What about Facebook, Instagram and other sites? And more than that, what about all the rest of hundreds of thousands websites which discuss the US elections? If “#VoteHillaryClinton” appears 100,000 times in Twitter, and “#VoteDonaldTrump” appears only 30,000 times in twitter, but “#VoteDonaldTrump” appears 500,000 more times in other small websites – it is trending higher than #VoteHillaryClinton. Ignoring “all the rest” is a big mistake, and basically misleading the way you read trends. It is like ignoring the “long tail” of your website traffic – sometimes it adds up to more than the “tall neck” of it!
A good example, in comparison, is the concept of a “search engine”. In the early ages, search worked on a specific website, not on all websites. You entered the university site, and you could search for papers. You couldn’t search for papers on ALL universities sites. Then the search engines (Lycos, Webcrawler, Google) came to light and offered a search that is searching on everything: it consolidates the search results of many websites, in one page of results.
TagPredict is basically announcing a revolution in the trends research industry which is similar to what search engines improved when they emerged: instead of looking at just ONE website when you look at the market trends, TagPredict is crawling to ALL websites, including small websites, blogs, news websites, personal websites, talkbacks, forums, etc – and consolidating the hashtags trends into one central platform.